> BUC-II: 22.02.16 - 24.02.16

Clik here to view the BUC-II poster with information about its venue which is: Unidad de Postgrado romm J306, UNAM main campus.

Overview

BUC-II continues from the previous one, developing the theme of Big Data and Statistics in Environmental Research. A team of six will come from Bath to deliver the course "Thinking Globally: The Role of Big Data" on the main UNAM campus. Academics Gavin Shaddick, Dan Simpson and Karim Anaya-Izquierdo will deliver the lectures and lab sessions will be guided by PhD students Dorka Fekete, Matt Thomas and Alice Davis.

The course provided an introduction to modelling relationships in both space and time with particular focus on fitting complex models to big data. The aim of the course is prove an interactive experience for students and researchers from a variety of fields and to allow them to experience state of the art statistical methodology and it’s application. The course covered both theory and applied examples, the latter specifically through practical ‘hands-on’ computer sessions in which participants will be guided through the analyses of real  data with both temporal and spatial structure.

The PhD Conference

The Course

The course focuses on five main topics.

• The need for spatial and temporal modelling and the role of big data
• Bayesian computation
• Regional modelling with big data
• Scalable spatial modelling
• Extending models to further dimensions

The course is aimed at postgraduate students, postdoctoral researchers, individuals working in industry or government in the field of statistics, exposure modelling or epidemiology and those who are interested in implemented advanced statistical modelling using big data.

Attendees of this course should exit the course with an understanding of the basic principles of hierarchical Bayesian modelling and of the need to account for spatial and temporal dependence in environmental research and, more generally, when modelling complex systems. In addition students will attain knowledge of how integrated nested Laplace approximations (INLA) can be used to implement methods for spatial and temporal modelling with high-dimensional data within a Bayesian framework.

Teaching will be based on a mixture of lectures, worked examples and computer-based practicals.Students will need to bring a laptop to the course with R and R-INLA software installed (details of how to access these materials will be provided on registration). Attendees may find the following refernce useful.

Spatio-Temporal Methods in Environmental Epidemiology by Gavin Shaddick and James V. Zidek, Chapman and Hall/CRC

Participants will be expected to have a basic understanding and experience of applying and interpreting multiple regression models. Familiarity with the R software will be assumed.

The timetable for the course is as follows.

Monday (February 22)

Tuesday (February 23)

Wednesday (February 24)

Further Details

Schedule, details, location background information, link to data, code, examples:

http://www.stat.ubc.ca/~gavin/STEPIBookNewStyle/course_unam.html

Participants